(1) Field of Invention
The present invention relates to a system for registering a viewpoint of an imaging sensor with respect to a geospatial model or map using contextual information.
(2) Description of Related Art
Image registration is the process of transforming different sets of data into one coordinate system. Previous methods for image or sensor viewpoint registration rely on matching features between the sensor-generated image and the larger image or model of the scene. These features are based on processed pixel values that encode some measure of local similarity in color, brightness, shape, texture, or spatial correlations in variations of these properties. This dependence on pixel values makes registration sensitive to variations in Sensor response, lighting, viewpoint, and modality.
Previous approaches, such as described by Nguyen et al. in “Spatio-Temporal Context for Robust Multitarget Tracking” in Institute of Electrical and Electronic Engineers (IEEE) Transactions on Pattern Analysis and Machine Intelligence, 29(1):52-64, have incorporated contextual information in the form of relative spatial positioning of the target among other targets in a neighborhood. However, the relationship between the behaviors of moving targets and the geospatial context was not utilized. The Baseline Tracker (BRAT), a multi-target tracking algorithm developed by the Air Force Research Laboratory, utilizes road map information to introduce pseudo-measurements for input to the Kalman tracker when the actual measurement is close to exactly one road. The purpose of the Kalman tracker is to use measurements observed over time, containing noise (i.e., random variations) and other inaccuracies, and produce values that tend to be closer to the true values of the measurements and their associated calculated values. The pairing is made by extending the principal component vector of the measurement error covariance an amount 3.5 times the principal component magnitude until it intersects the road. The BRAT method uses the road map in another way by setting the heading of the target along the long axis of the road. Further, the BRAT method is deterministic and not probabilistic and is, therefore, limited in its robustness to noise and suboptimal in the ad hoc manner of incorporating additional contextual information. The BRAT method also does not utilize the concept of using the linkage between target behavior and map data for sensor registration.
Thus, a continuing need exists for a system and method of sensor viewpoint registration which utilizes the relationship between expected object behaviors and geospatial context.